Big Earth Data boost UN SDGs

National-scale 10-m maps of cropland use intensity in China during 2018-2023

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018-2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

Regularity of rural settlement changes driven by rapid urbanization in North China over the three decades

The systematic decline of rural areas in the process of rapid urbanization has become a global trend, creating greater challenges for sustainable rural development. As the spatial projection of socio-economic development and living environment in rural areas, the continuous tracking of rural settlements (RUS) is crucial to quantify the imbalance of rural development. However, consistent information on RUS is highly needed but is quite deficient in current research. In this study, a cost-effective mapping model was proposed to produce an annual RUS dataset in the rapid urbanization region of Beijing-Tianjin-Hebei (BTH) in North China during 1990-2020, and the temporal-spatial regularity of RUS changes was further analyzed. The location-based and the area-based comparison verified the effectiveness of our model, with a mean overall accuracy of 85% and a mean correlation value of 0.88, respectively. The total area of RUS in the BTH region increased by 2561 km2 from 1990 to 2020, while the average size of RUS remained stable after 2005. The annual change trends in RUS appeared with increasing and decreasing accounting for 76.33% and 23.67%, respectively. The centroids of RUS in Tianjin and Hebei have moved closer to Beijing, while those in Beijing have moved away from the former. Notably, we have identified 56.3% counties in the BTH region belong to the "Convex-I" change type in RUS. In general, our work can help to consistently quantify the spatiotemporal patterns of RUS in a cost-effective way, providing more explicit spatial information and continuous temporal information for rural residential land management.